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https://issues.apache.org/jira/browse/SPARK-15526?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15301298#comment-15301298
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Sean Owen commented on SPARK-15526:
-----------------------------------
I've also shaded JPMML in an app in order to work around the fact that Spark
uses 1.1 in older versions. While there are many libs we use that we should
probably shade and don't, well, this is one of them and I think it's pretty
unproblematic to shade. I expect many ML apps will want to use JPMML. Overall
therefore I'd favor this.
> Shade JPMML
> -----------
>
> Key: SPARK-15526
> URL: https://issues.apache.org/jira/browse/SPARK-15526
> Project: Spark
> Issue Type: Dependency upgrade
> Components: ML, MLlib
> Affects Versions: 2.0.0
> Reporter: Villu Ruusmann
> Priority: Minor
> Original Estimate: 2h
> Remaining Estimate: 2h
>
> The Spark-MLlib module depends on the JPMML-Model library
> (org.jpmml:pmml-model:1.2.7) for its PMML export capabilities. The
> JPMML-Model library is included in the Apache Spark assembly, which makes it
> very difficult to build and deploy competing PMML exporters that may wish to
> depend on different versions (typically much newer) of the same library.
> JPMML-Model library classes are not part of Apache Spark public APIs, so it
> shouldn't be a problem if they are relocated by prepending a prefix
> "org.spark_project" to their package names using Maven Shade Plugin. The
> requested treatment is identical to how Google Guava and Jetty dependencies
> are shaded in the final assembly.
> This issue is raised in relation to the JPMML-SparkML project
> (https://github.com/jpmml/jpmml-sparkml), which provides PMML export
> capabilities for Spark ML Pipelines. Currently, application developers who
> wish to use it must tweak their application classpath, which assumes
> familiarity with build internals.
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